Forests and floods: A new paradigm sheds light on age‐old controversies
Bibliographic record
Abstract
The science of forests and floods is embroiled in conflict and is in urgent need of reevaluation in light of changing climates, insect epidemics, logging, and deforestation worldwide. Here we show how an inappropriate pairing of floods by meteorological input in analysis of covariance (ANCOVA) and analysis of variance (ANOVA), statistical tests used extensively for evaluating the effects of forest harvesting on floods smaller and larger than an average event, leads to incorrect estimates of changes in flood magnitude because neither the tests nor the pairing account for changes in flood frequency. We also illustrate how ANCOVA and ANOVA, originally designed for detecting changes in means, do not account for any forest harvesting induced change in variance and its critical effects on the frequency and magnitude of larger floods. The outcomes of numerous studies, which applied ANCOVA and ANOVA inappropriately, are based on logical fallacies and have contributed to an ever widening disparity between science, public perception, and often land‐management policies for decades. We demonstrate how only an approach that pairs floods by similar frequency, well established in other disciplines, can evaluate the effects of forest harvesting on the inextricably linked magnitude and frequency of floods. We call for a reevaluation of past studies and the century‐old, preconceived, and indefensible paradigm that shaped our scientific perception of the relation between forests, floods, and the biophysical environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.072 |
| Scholarly communication | 0.010 | 0.030 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".